scientific-ensemble-methods
CommunityBoost prediction accuracy with ensemble methods.
Authornahisaho
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Design and evaluate robust ensemble strategies to improve predictive performance and stability across models.
Core Features & Use Cases
- Gradient boosting support: integrates XGBoost, LightGBM, and CatBoost for classification and regression.
- Stacking and blending: builds multi-stage ensembles and meta-models to improve accuracy.
- Voting ensembles: soft/hard voting across diverse base models for stable predictions.
- Diversity evaluation: measures ensemble diversity using Q-statistic and disagreement metrics.
- Pipeline integration: outputs include stacking_meta.pkl, boosting_comparison.csv, and ensemble_diversity.json.
- OpenML benchmarking: leverages ToolUniverse OpenML references for benchmarking ensemble methods.
Quick Start
Provide your training data and run this skill to compare boosting models, execute stacking, and assess ensemble diversity.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: scientific-ensemble-methods Download link: https://github.com/nahisaho/satori/archive/main.zip#scientific-ensemble-methods Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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